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Mundy, M.

Publications and source records attributed to Mundy, M..

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Distinct Microbes, Metabolites, and Ecologies Define the Microbiome in Deficient and Proficient Mismatch Repair Colorectal Cancers

Background\n\nThe link between colorectal cancer (CRC) and the gut microbiome has been established, but the specific microbial species and their role in carcinogenesis remain controversial. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology.\n\nMethods\n\nWe collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra-subject sample correlation, and read depth. Genome-scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks.\n\nResults\n\nWe demonstrate distinct roles for microbes in dMMR and pMMR CRC. Sulfidogenic Fusobacterium nucleatum and hydrogen sulfide production were significantly enriched in dMMR CRC, but not pMMR CRC. We also surveyed the butyrate-producing microbial species, but did not find a significant difference in predicted or actual butyrate production between dMMR and pMMR microbial communities. Finally, we observed that dMMR microbial communities were predicted to be less stable than pMMR microbial communities. Community stability may play an important role in CRC development, progression, or immune activation within the respective MMR subtypes.\n\nConclusions\n\nIntegrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers.

cancer biology

ProbAnnoWeb and ProbAnnoPy: probabilistic annotation and gap-filling of metabolic reconstructions

SummaryGap-filling is a necessary step to produce quality genome-scale metabolic reconstructions capable of flux-balance simulation. Most available gap-filling tools use an organism-agnostic approach, where reactions are selected from a database to fill gaps without consideration of the target organism. Conversely, our likelihood based gap-filling with probabilistic annotations selects candidate reactions based on a likelihood score derived specifically from the target organisms genome. Here, we present two new implementations of probabilistic annotation and likelihood based gap-filling: a web service called ProbAnnoWeb, and a standalone python package called ProbAnnoPy.\n\nAvailability and ImplementationOur tools are available as a web service with no installation needed (ProbAnnoWeb), available at http://probannoweb.systemsbiology.net, and as a local python package implementation (ProbAnnoPy), available for download at http://github.com/PriceLab/probannopy.\n\nContacthttp://Evangelos.Simeonidis@systemsbiology.org; http://Nathan.Price@systemsbiology.org

systems biology